Abstract
A new method is used in this work to classify ECG beats. The new method is about using an optimization algorithm for selecting the features of each beat then classify them. For each beat, twenty-four higher order statistical features and three timing interval features are obtained. Five types of beat classes are used for classification in this work, atrial premature contractions (APC), normal (NOR), premature ventricular contractions (PVC), left bundle branch (LBBB) and right bundle branch (RBBB). Cuttlefish algorithm is used for feature selection which is a new bio-inspired optimization algorithm. Four classifiers are used within CFA, Scaled Conjugate Gradient Artificial Neural Network (SCG-ANN), K-Nearest Neighborhood (KNN), Interactive Dichotomizer 3 (ID3) and Support Vector Machine (SVM). The final results show an accuracy of 97.96% for ANN, 95.71% for KNN, 94.69% for ID3 and 93.06% for SVM, these results were tested on fourteen signal records from MIT-HIH database, where 1400 beats were extracted from these records.
Highlights
Automatic diagnosis of electrocardiogram (ECG) it is very important in the field of heart disease diagnosis, that is why feature extraction and classification it is an important step to achieve a good diagnosis [1, 2].Many techniques have been proposed to classify ECG beat using data preprocessing, feature extraction, and classification algorithms
Fourteen ECG records were used and distributed on five classes as in table 1, 100 beat is extracted from 106 and 223 records for atrial premature contractions (APC), a total of 300 beats were selected from NOR records (100, 105 and 215) 100 from each, for premature ventricular contractions (PVC) records (207, 209 and 232) 100 beat is selected from each record, for LBBB records (109, 111 and 214) 100 beat is selected from each record, and for RBBB records (118, 124 and 212) 100 beat is selected from each record
This work is about using a hybrid technique for classifying ECG signals into five classes, using 27 features (24 statistical with 3-time interval features)
Summary
Automatic diagnosis of electrocardiogram (ECG) it is very important in the field of heart disease diagnosis, that is why feature extraction and classification it is an important step to achieve a good diagnosis [1, 2]. Many techniques have been proposed to classify ECG beat using data preprocessing, feature extraction, and classification algorithms. Ataollah Ebrahimzadeh and Ali Khazaee they have used wavelet transform and time interval features with radial base function for classification of five types of beats [4]. Ebrahimzadeh, Shakiba and Khazaee used higher order statistics with time interval features with radial base function and bees algorithm for classification of 5 beats [1]. Alan and Majd have proposed a method of using higher order statistics with time intervals as features and Artificial Neural Networks to classify 5 arrhythmia beat types [10]. Another strategy has been proposed in this work.
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